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LeadIntel

LeadIntel

by rushikesheleven
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

find_business_website

Find and verify a business's official website by searching local knowledge and free web results, filtering out directories, social media, and aggregators to isolate the primary domain.

Instructions

    Find and verify the official website of a business.

    Searches local knowledge providers and free web search (no paid API key required).
    Filters out directories, social media links, and aggregators to isolate the primary domain.

    Args:
        business_name: Name of the business (e.g. 'Blue Bottle Coffee', 'Acme Corp').
        location: Optional location context (e.g. 'San Francisco', 'London').
        business_id: Optional unique LeadIntel/OSM ID (e.g. 'osm_node_123', 'demo_001').

    Returns:
        Dict containing:
          - found (bool): True if an official website was identified
          - business_name (str): Input business name
          - location (str|null): Input location
          - website (str|null): Discovered website URL
          - discovery_method (str): Method used ('provider:openstreetmap', 'duckduckgo_lite', etc.)
          - note (str|null): Informative status note

Input Schema

NameRequiredDescriptionDefault
locationNo
business_idNo
business_nameYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.1/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations supplied, the description carries the full burden and discloses real behavior: the discovery sources, the exclusion of directories/social/aggregators to isolate a primary domain, and the discovery_method field that tells the caller how the result was obtained. It omits failure/ambiguity handling (e.g., multiple candidate domains) and any latency or rate-limit expectations, which keeps it short of a 5.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The opening sentence is front-loaded with the purpose, followed by mechanism, then structured Args/Returns blocks. The parameter listing duplicates the schema's property names, but given 0% schema coverage that repetition is functional rather than wasteful.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the Returns block is genuinely necessary and covers each returned field including the nullable website and the discovery_method enum-like values. What is missing is guidance on the disambiguation path (what happens on multiple or zero matches) and parameter precedence, given a modest 3-parameter tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate, and it does: all three parameters are documented with concrete examples ('Blue Bottle Coffee', 'San Francisco', 'osm_node_123'). It does not explain how business_id interacts with business_name or whether one takes precedence, nor how location constrains the search.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource ('Find and verify the official website of a business') and sharpens the scope by explaining that directories, social links, and aggregators are filtered out. That filtering detail alone makes it distinguishable from sibling tools like find_social_profiles and analyze_website.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is some useful context (it uses local knowledge providers plus free web search, so no paid API key is required), which implies it is the cheap first step before analyze_website. However, it never explicitly says when to prefer this over its siblings, when not to use it, or what to do if the name is ambiguous.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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